Abstract The Bundesgerichtshof (BGH), the German Federal Court of Justice, is the highest court of appeal in civil and criminal matters in Germany. BGH judgments receive regular and intense scrutiny, but German doctrinal scholars routinely focus only on a single decision or a small set of decisions. Quantitative approaches are often infeasible because of lack of data. We present two original datasets on the German Federal Court of Justice: 1) a corpus of all 77,892 judgments in civil and criminal matters for the period 2000-2024 with 36 variables plus the internal citation network and 2) a corpus of 36,316 judgments in criminal matters for the period 1950-1999 with 31 variables. We use state-of-the-art data engineering techniques to construct reproducible extract-transform-load (ETL) pipelines that automatically acquire, clean, test, analyze and document German Federal Court of Justice data from the official BGH database and an older criminal justice dataset provided to the authors. Citations are extracted from judgment full texts, analyzed and provided in GraphML format. Despite centuries of intense academic engagement with the law, quantitative and empirical approaches remain exceedingly rare in Germany. Lack of data, lack of training, lack of awareness and many other reasons have caused this situation. Solving the data problem is a first step to empowering German lawyers in the use of quantitative methods and may help with an empirical turn. Context The presentation was delivered by Seán Fobbe at the ESELS Conference Toulouse 2025 in Panel 2.2 on 19 June 2025. About the Speaker Seán Fobbe is a researcher at Ludwig-Maximilian-University of Munich (Germany). He specializes in international human rights law, humanitarian law and international criminal law, with a particular emphasis on the protection of cultural heritage in Iraq and the prosecution of atrocity crimes committed by ISIS/ISIL. As a data scientist, his interests lie in natural language processing (NLP), quantitative peace research, data engineering and statistical computing with the R programming language. Personal Website: www.seanfobbe.com